Robust Optimization Over Time: A Critical Review

Yazdani, D; Omidvar, MN; Yazdani, D; Branke, J; Nguyen, TT; Gandomi, AH; Jin, Y; Yao, X

Robust Optimization Over Time: A Critical Review

Yazdani, D

Omidvar, MN Yazdani, D

Branke, J Nguyen, TT Gandomi, AH Jin, Y Yao, X

Permalink

Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Evolutionary Computation, 2023, PP, (99), pp. 1-1
Issue Date:: 2023-01-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Published versionAdobe PDF (7.73 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Yazdani, D https://orcid.org/0000-0002-7799-5013
dc.contributor.author	Omidvar, MN
dc.contributor.author	Yazdani, D https://orcid.org/0000-0002-7799-5013
dc.contributor.author	Branke, J
dc.contributor.author	Nguyen, TT
dc.contributor.author	Gandomi, AH
dc.contributor.author	Jin, Y
dc.contributor.author	Yao, X
dc.date.accessioned	2024-03-03T02:03:36Z
dc.date.available	2024-03-03T02:03:36Z
dc.date.issued	2023-01-01
dc.identifier.citation	IEEE Transactions on Evolutionary Computation, 2023, PP, (99), pp. 1-1
dc.identifier.issn	1089-778X
dc.identifier.issn	1941-0026
dc.identifier.uri	http://hdl.handle.net/10453/176019
dc.description.abstract	Robust optimization over time (ROOT) is the combination of robust optimization and dynamic optimization. In ROOT, frequent changes to deployed solutions are undesirable, which can be due to the high cost of switching between deployed solutions, limitations on the resources required to deploy new solutions, and/or the system’s inability to tolerate frequent changes in the deployed solutions. ROOT is dedicated to the study and development of algorithms capable of dealing with the implications of deploying or maintaining solutions over longer time horizons involving multiple environmental changes. This paper presents an in-depth review of the research on ROOT. The overarching aim of this survey is to help researchers gain a broad perspective on the current state of the field, what has been achieved so far, and the existing challenges and pitfalls. This survey also aims to improve accessibility and clarity by standardizing terminology and unifying mathematical notions used across the field, providing explicit mathematical formulations of definitions, and improving many existing mathematical descriptions. Moreover, we classify ROOT problems based on two ROOT-specific criteria: the requirements for changing or keeping deployed solutions and the number of deployed solutions. This classification helps researchers gain a better understanding of the characteristics and requirements of ROOT problems, which is crucial to systematic algorithm design and benchmarking. Additionally, we classify ROOT methods based on the approach they use for finding robust solutions and provide a comprehensive review of them. This survey also reviews ROOT benchmarks and performance indicators. Finally, we identify several future research directions.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Evolutionary Computation
dc.relation.isbasedon	10.1109/TEVC.2023.3306017
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0806 Information Systems, 0906 Electrical and Electronic Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	4602 Artificial intelligence
dc.title	Robust Optimization Over Time: A Critical Review
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0806 Information Systems
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
dc.date.updated	2024-03-03T02:03:30Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Robust optimization over time (ROOT) is the combination of robust optimization and dynamic optimization. In ROOT, frequent changes to deployed solutions are undesirable, which can be due to the high cost of switching between deployed solutions, limitations on the resources required to deploy new solutions, and/or the system’s inability to tolerate frequent changes in the deployed solutions. ROOT is dedicated to the study and development of algorithms capable of dealing with the implications of deploying or maintaining solutions over longer time horizons involving multiple environmental changes. This paper presents an in-depth review of the research on ROOT. The overarching aim of this survey is to help researchers gain a broad perspective on the current state of the field, what has been achieved so far, and the existing challenges and pitfalls. This survey also aims to improve accessibility and clarity by standardizing terminology and unifying mathematical notions used across the field, providing explicit mathematical formulations of definitions, and improving many existing mathematical descriptions. Moreover, we classify ROOT problems based on two ROOT-specific criteria: the requirements for changing or keeping deployed solutions and the number of deployed solutions. This classification helps researchers gain a better understanding of the characteristics and requirements of ROOT problems, which is crucial to systematic algorithm design and benchmarking. Additionally, we classify ROOT methods based on the approach they use for finding robust solutions and provide a comprehensive review of them. This survey also reviews ROOT benchmarks and performance indicators. Finally, we identify several future research directions.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/176019